





import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets




def     A_      (	lowercase_	)      ->    str:
       return 1 / (1 + np.exp(-z	))




def     A_      (	lowercase_   ,		lowercase_	)      ->    int:
       return (-y * np.log(UpperCamelCase_	) - (1 - y) * np.log(1 - h	)).mean()




def     A_      (	lowercase_   ,		lowercase_   ,		lowercase_	)      ->    Tuple:
       _snake_case   :				List[Any]  					=    np.dot(UpperCamelCase_   ,		UpperCamelCase_	)
       return np.sum(y * scores - np.log(1 + np.exp(UpperCamelCase_	)	)	)




def     A_      (	lowercase_   ,		lowercase_   ,		lowercase_   ,		lowercase_=70000	)      ->    Any:
       _snake_case   :				Optional[int]  					=    np.zeros(x.shape[1]	)

       for iterations in range(UpperCamelCase_	):
              _snake_case   :				Union[str, Any]  					=    np.dot(UpperCamelCase_   ,		UpperCamelCase_	)
              _snake_case   :				Optional[Any]  					=    sigmoid_function(UpperCamelCase_	)
              _snake_case   :				Union[str, Any]  					=    np.dot(x.T   ,		h - y	) / y.size
              _snake_case   :				Dict  					=    theta - alpha * gradient  # updating the weights
              _snake_case   :				Optional[Any]  					=    np.dot(UpperCamelCase_   ,		UpperCamelCase_	)
              _snake_case   :				List[str]  					=    sigmoid_function(UpperCamelCase_	)
              _snake_case   :				Optional[Any]  					=    cost_function(UpperCamelCase_   ,		UpperCamelCase_	)
              if iterations % 100 == 0:
                     print(f'''loss: {j} \t'''	)  # printing the loss after every 100 iterations
       return theta


# In[68]:

if __name__ == "__main__":
   lowerCAmelCase_				= datasets.load_iris()
   lowerCAmelCase_				= iris.data[:, :2]
   lowerCAmelCase_				= (iris.target != 0) * 1

   lowerCAmelCase_				= 0.1
   lowerCAmelCase_				= logistic_reg(alpha, x, y, max_iterations=7_0_0_0_0)
   print("theta: ", theta)  # printing the theta i.e our weights vector




   def     A_      (	lowercase_	)      ->    Tuple:
          return sigmoid_function(
              np.dot(UpperCamelCase_   ,		UpperCamelCase_	)	)  # predicting the value of probability from the logistic regression algorithm

   plt.figure(figsize=(1_0, 6))
   plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="b", label="0")
   plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="r", label="1")
   (lowerCAmelCase_)				= (x[:, 0].min(), x[:, 0].max())
   (lowerCAmelCase_)				= (x[:, 1].min(), x[:, 1].max())
   (lowerCAmelCase_)				= np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
   lowerCAmelCase_				= np.c_[xxa.ravel(), xxa.ravel()]
   lowerCAmelCase_				= predict_prob(grid).reshape(xxa.shape)
   plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="black")

   plt.legend()
   plt.show()











'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_camembert import CamembertTokenizer
else:
			SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  None


SCREAMING_SNAKE_CASE__     :						Tuple	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						List[Any]	=  {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE__     :						List[Any]	=  {
    '''camembert-base''': 5_1_2,
}

SCREAMING_SNAKE_CASE__     :						Any	=  '''▁'''

class 							a__( snake_case__     ):
			a_	: Tuple									=      VOCAB_FILES_NAMES
			a_	: Optional[int]									=      PRETRAINED_VOCAB_FILES_MAP
			a_	: List[str]									=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_	: str									=      ['''input_ids''', '''attention_mask''']
			a_	: Union[str, Any]									=      CamembertTokenizer






			def __init__(     self					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="<unk>"					,   _UpperCAmelCase="<pad>"					,   _UpperCAmelCase="<mask>"					,   _UpperCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]					,   **_UpperCAmelCase					,   )							->							str:
					# Mask token behave like a normal word, i.e. include the space before it
					snake_case__	     =AddedToken(_UpperCAmelCase					,   lstrip=_UpperCAmelCase					,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						) else mask_token

					super().__init__(
					    _UpperCAmelCase					,   tokenizer_file=_UpperCAmelCase					,   bos_token=_UpperCAmelCase					,   eos_token=_UpperCAmelCase					,   sep_token=_UpperCAmelCase					,   cls_token=_UpperCAmelCase					,   unk_token=_UpperCAmelCase					,   pad_token=_UpperCAmelCase					,   mask_token=_UpperCAmelCase					,   additional_special_tokens=_UpperCAmelCase					,   **_UpperCAmelCase					,   )

					snake_case__	     =vocab_file
					snake_case__	     =False if not self.vocab_file else True






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							List[int]:

					if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					snake_case__	     =[self.cls_token_id]
					snake_case__	     =[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							List[int]:
					snake_case__	     =[self.sep_token_id]
					snake_case__	     =[self.cls_token_id]

					if token_ids_a is None:
							return len(cls + token_ids_a + sep						) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							Tuple[str]:
					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
							    'tokenizer.'						)

					if not os.path.isdir(_UpperCAmelCase						):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
							return
					snake_case__	     =os.path.join(
					    _UpperCAmelCase					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

					if os.path.abspath(self.vocab_file						) != os.path.abspath(_UpperCAmelCase						):
							copyfile(self.vocab_file					,   _UpperCAmelCase						)

					return (out_vocab_file,)




